Automated security and surveillance systems typically employ video cameras or other image capturing devices or sensors to collect image data. In the simplest systems, images represented by the image data are displayed for contemporaneous screening by security personnel and/or recorded for later reference after a security breach. In those systems, the task of detecting objects of interest is performed by a human observer. A significant advance occurs when the system itself is able to perform object detection and classification, either partly or completely.
In a typical surveillance system, for example, one may be interested in detecting objects such as humans, vehicles, animals, etc. that move through the environment. Different objects might pose different threats or levels of alarm. For example, an animal in the scene may be normal, but a human or vehicle in the scene may be cause for an alarm and may require the immediate attention of a security guard. Existing systems capable of classifying detected objects tend to use simple heuristics to distinguish broad categories of objects from one another. For example, pre-determined expectations for aspect ratio and height are used to classify a detected object as a human being. Theoretically, heuristic methods are computationally inexpensive and easy to implement, but they are much less robust than optimized parametric classifiers formed by using known machine learning algorithms such as Adaptive Boosting (AdaBoost). Known parametric classifiers, however, suffer from one or more of (1) lack of labeled data for training and (2) inability to automatically evolve.
Prior art classifiers typically require manual geometric calibration and tuning. Such calibration and tuning typically focuses on intermediate user input (e.g., object heights) that indirectly influence the system's performance and typically requires time-consuming labor by trained personnel during installation. Moreover, retuning and calibration is typically needed as the seasons change or if a camera is moved.